Reducing Unnecessary Imaging in Children With Multicystic Dysplastic Kidney or Solitary Kidney
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES Children with isolated unilateral multicystic dysplastic kidney (MCDK) or congenital solitary kidney (CSK) undergo serial renal ultrasonography with variable frequency until they are transitioned to adult care. A growing body of literature suggests the value of frequent ultrasonography in this population is limited, providing no benefit to overall outcomes. Despite emerging evidence, ultrasound remains overused, resulting in avoidable health care expenditures and unnecessary use of resources. With our initiative, we aimed to improve quality of care by reducing avoidable ultrasounds in these children. METHODS This was a single-center, prospective, interrupted time series of children <18 years with ultrasound-confirmed isolated unilateral MCDK or CSK in the outpatient nephrology clinic to evaluate the effect of a decision-making algorithm on the proportion of children receiving an avoidable ultrasound. An algorithm depicting a consensus, evidence-based protocol for managing pediatric MCDK or CSK was refined through content expert feedback and usability testing to standardize frequency of ultrasonography. Ultrasounds were deemed necessary after birth, at 6 months, and at 2, 5, 10, and 15 years. Differences pre- and postintervention were determined by using a U chart and t and F tests for significance. RESULTS The algorithm resulted in a 47% reduction (P < .001) in the proportion of avoidable ultrasounds ordered in children with MCDK and CSK. This reduction was sustainable over a 6-month period and would result in at least $46 000 annual savings. CONCLUSIONS Introduction of a clinical decision-making algorithm was associated with a reduction in avoidable ultrasound testing. Improving adherence across providers may allow for an even more pronounced reduction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".